[Limdep Nlogit List] How to deal with large numbers of attributes?

[David Hensher]

This relates to the literature on attribute non attendance where different attributes are relevant to different people and selecting a limited set initially without strong evidence of the universal relevant set is behaviourally concerning.

Depending on how many attributes, up to 20 or so is fine and one can ask questions on which attributes are attended to. Lots of papers on this by people such as Hensher, Louviere, Scarpa, and the special issue a couple of years ago in J of choice modelling on process heuristics and especially the design of designs (DoD) approach initially developed by Hensher

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On 17 Feb 2018, at 7:33 am, Mikołaj Czajkowski <miq at wne.uw.edu.pl<mailto:miq at wne.uw.edu.pl>> wrote:


Dear Richard,

It seems to me like the answer to this question would depend on the goal of the modeller - whether he wants to learn a lot about the most important attributes only, or have some idea about all the attributes. I am not sure is a lot of concrete advice can be given in these kinds of situations.

Cheers,
Mik


On 2018-02-16 21:20, Richard Turner wrote:
Greetings,

What is the best way to handle large numbers of attributes in discrete
choice experiments?

Is it better to do a *partial profile design* or to do some* two-step
approach* such as conducting  an "initial study" using a partial profile
design, then conduct a final study using the most important attributes,
which were derived from the initial study (implicit in the second method
would be to synthesize the learnings from both studies to get some ranking
of all the attributes)?

I've done some searching, but haven't found any "defining" papers on the
subject.

Any advice and/or direction is greatly appreciated!

Regards,

Richard
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